Applications In Computer Science Research Articles

The Logic and Application of Greedy Algorithms

Abstract. Being easy to implement and offering faster solutions, the greedy algorithms have widely been used in solving combinatorial optimization problems in computer science and many practical applications, such as resource allocation and data compression. This paper aims at analyzing the definitions, theory, and application of greedy algorithms in computer science as well as their logic and efficacy in numerous situations. In this analysis, concepts such as the greedy choice property and optimal substructure are examined as essential to understanding how greedy algorithms operate. The provided analysis mainly assumes prior knowledge of concepts and problems like the Fractional Knapsack problem and the Shortest Path problem solution as well as some practical problems, for example, resource allocation and the exact coin change problem. Analytical data and real applications are used to analyze the performance of greedy algorithms vis-a-vis other categories, such as the dynamic programming paradigm. The results reveal that greedy algorithms have the potential for great efficiency while also being restricted by certain parameters. They are ideal in the following aspects pertaining to computational aspects, that is, speed, ease of implementation, and applicability to large datasets. But they do not necessarily promise strong optimality in certain situations. The conclusion notes that greedy algorithms deserve further applications when the specified conditions are met and states that the effectiveness of these conditions needs to be studied.

The number of spanning trees in K n -chain and ring graphs

The problem of counting spanning trees of graphs or networks is a fundamental and crucial area of research in combinatorics, while has numerous important applications in statistical physics, network theory and theoretical computer science. Very recently, Kosar, Zaman, Ali and Ullah obtained a nice formula on the number of spanning trees of a K 5-chain network K5ℓ constructed by connecting ℓcopys of complete graphs K 5. They made extensive use of matrix theory and spectral graph theory, especially the normalized Laplacian of graphs. In this paper, by using a rather simple and more physical treatment (the mesh-star transformation in electrical network) without any linear algebra, we generalize their result to K n -chain graphs and K n -ring graphs. The results show that there is a simple relation between the number of spanning trees of the K n -chain graph Lnℓ and the K n -ring graph Cnℓ . We also calculate the corresponding tree entropy (or so called ‘the asymptotic growth constant’) and find that the tree entropy of the corresponding K n -chain graphs and K n -ring graphs are totally the same.

A Comprehensive Survey on the Role of Law in Different Applications in Computer Science

One significant tool for addressing the concerns associated with the use of one of the applications in computer science is the law. Numerous laws and rules governing the use of computer science are designed to safeguard users, customers, and society at large. Law uses norms created by governmental and social institutions to control behavior. Programming is used to explore digital information in computer science. Computers got more potent and smaller as they evolved through generations, utilizing various technologies such as integrated circuits. Cyber law regulates online behavior and deals with matters pertaining to intellectual property, privacy, domain names, and other legal concerns. A few of the many concerns that the law must address in order to mitigate the electronic crimes are protecting privacy and security, upholding justice, limiting civil and criminal liability, ensuring safety, and controlling the application of AI in the workplace. Guidelines that people and organizations have to follow when utilizing computer science applications, making sure that these requirements are met in full. Additionally, as companies must commit to recording AI usage procedures and making clear the use of data and algorithms, the regulation fosters accountability and openness. By doing this, the possibility of prejudice and mistakes when utilizing AI is decreased. This survey provides a self-contained introduction to cybercrimes and types of cybercrimes. We also present ways to combat cybercrime and limit its spread.

Ai] Explore!: An Educational Game for Developing Programming Skills and Algorithmic Thinking

The game [ai] explore! was developed to introduce the algorithm behind the popular science exhibition. The idea behind the [ai] explore! exhibition was to show the application of mathematics and computer science in solving real engineering problems by creating the exhibit using the mathematical algorithm Heat Equation Driven Area Coverage (HEDAC). The algorithm is based on scientific research and has many applications: exploration and search in unknown space, painting, planning agricultural spraying, scanning 3D objects, etc. The exhibits show the process by which the algorithm paints objects with a virtual brush and explores their shape. By surveying the high school students, it was shown that new mathematical and engineering results can be explained and turned into a playable game. After playing the game, students respond positively to the game presented and state that they have a good understanding of the HEDAC algorithm presented and how it works. This motivated us to investigate [ai] explore! as an educational game for teaching mathematics and computer science. The aim of the research described in this paper is to investigate the potential of the developed game as an educational game. The game-based learning (GBL) approach is increasingly used to increase student motivation and engagement in STEM lessons. The main research question is: How do primary school teachers and students perceive the usefulness of the game [ai] explore! in supporting teaching and learning of computer science concepts? We have developed and implemented teaching scenarios on how the game [ai] explore! can be used as an educational game in computer science lessons. We show that the game has great potential to teach primary school students programming. In addition, the game promotes algorithmic thinking and computational thinking skills in general, which are valuable not only in computer science but also in other fields such as mathematics, where a systematic and analytical approach to problem-solving is required.

On separability of semigroups: application to models of physics and discrete optimization

Nilpotent semigroups are used in various fields of physics to model processes like system decay in quantum mechanics, state transitions in statistical mechanics, and simplifying dynamical systems. They also assist in optimizing control processes. The set of nilpotent matrices forms a semigroup and plays a key role in Jordan decomposition, which has many physical applications. In quantum mechanics, the Jordan form helps analyze linear operators on Hilbert spaces, especially with systems involving eigenvalue multiplicities. In vibration analysis, it aids in studying normal modes of mechanical systems with degenerate frequencies. Additionally, Jordan decomposition simplifies control theory for linear time-invariant systems, stability analysis in dynamical systems, and the behavior of coupled oscillators, as well as solving systems of linear differential equations. Certainly, the semigroup theory has even more applications in theoretical computer science: suffice it to say, for example, that some authors identify semigroups and finite automata. In this paper, we address the problem of P-separability of semigroups with respect to three key predicates: equality-separability, divisibility-separability, and subsemigroup-separability, achieved through homomorphisms into a nilpotent semigroup. We present some significant results that advance the understanding of these concepts.

Development and applications of computer science in Poland during the communist rule

Development and applications of computer science in Poland during the communist rule

A survey of deep causal models and their industrial applications

The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continous advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models based on neural networks, and its core contributions are as follows: (1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; (2) we outline some typical applications of causal effect estimation to industry; (3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.

Pompeiu-Hausdorff Fuzzy <i>b</i>-metric Spaces Are Associated with a Common Fixed Point and Multivalued Mappings

The notion of fuzzy logic was introduced by Zadeh. Unlike traditional logic theory, where an element either belongs to the set or does not, in fuzzy logic, the affiliation of the element to the set is expressed as a number from the interval [0, 1]. The study of the theory of fuzzy sets was prompted by the presence of uncertainty as an essential part of real-world problems, leading Zadeh to address the problem of indeterminacy. The theory of a fixed point in fuzzy metric spaces can be viewed in different ways, one of which involves the use of fuzzy logic. Fuzzy metric spaces, which are specific types of topological spaces with pleasing ”geometric” characteristics, possess a number of appealing properties and are commonly used in both pure and applied sciences. Metric spaces and their various generalizations frequently occur in computer science applications. For this reason, a new space called a Pompeiu-Hausdorff fuzzy <i>b</i>-metric space is constructed in this paper. In this space, some new fixed point results are also formulated and proven. Additionally, a general common fixed point theorem for a pair of multi-valued mappings in Pompeiu-Hausdorff fuzzy <i>b</i>-metric spaces is investigated. The findings obtained in fuzzy metric spaces, such as those discussed in Remark 3.1, are generalized by the results in this paper, and additional specific findings are produced and supported by examples. The study of denotational semantics and their applications in control theory using fuzzy <i>b</i>-metric spaces and Pompeiu-Hausdorff fuzzy <i>b</i>-metric spaces will be an important next step.

The ultrametric backbone is the union of all minimum spanning forests

Minimum spanning trees and forests are powerful sparsification techniques that remove cycles from weighted graphs to minimize total edge weight while preserving node reachability, with applications in computer science, network science, and graph theory. Despite their utility and ubiquity, they have several limitations, including that they are only defined for undirected networks, they significantly alter dynamics on networks, and they do not generally preserve important network features such as shortest distances, shortest path distribution, and community structure. In contrast, distance backbones, which are subgraphs formed by all edges that obey a generalized triangle inequality, are well defined in directed and undirected graphs and preserve those and other important network features. The backbone of a graph is defined with respect to a specified path-length operator that aggregates weights along a path to define its length, thereby associating a cost to indirect connections. The backbone is the union of all shortest paths between each pair of nodes according to the specified operator. One such operator, the max function, computes the length of a path as the largest weight of the edges that compose it (a weakest link criterion). It is the only operator that yields an algebraic structure for computing shortest paths that is consistent with De Morgan's laws. Applying this operator yields the ultrametric backbone of a graph in that (semi-triangular) edges whose weights are larger than the length of an indirect path connecting the same nodes (i.e. those that break the generalized triangle inequality based on max as a path-length operator) are removed. We show that the ultrametric backbone is the union of minimum spanning forests in undirected graphs and provides a new generalization of minimum spanning trees to directed graphs that, unlike minimum equivalent graphs and minimum spanning arborescences, preserves all shortest paths and De Morgan's law consistency.

Fast Algorithms for \(\ell_p\)-Regression

The ℓ p -norm regression problem is a classic problem in optimization with wide ranging applications in machine learning and theoretical computer science. The goal is to compute \(\boldsymbol {\mathit {x}}^{\star } =\arg \min _{\boldsymbol {\mathit {A}}\boldsymbol {\mathit {x}}=\boldsymbol {\mathit {b}}}\Vert \boldsymbol {\mathit {x}}\Vert _p^p \) , where \(\boldsymbol {\mathit {x}}^{\star }\in \mathbb {R}^n,\boldsymbol {\mathit {A}}\in \mathbb {R}^{d\times n},\boldsymbol {\mathit {b}} \in \mathbb {R}^d \) and d ≤ n . Efficient high-accuracy algorithms for the problem have been challenging both in theory and practice and the state-of-the-art algorithms require \(poly(p)\cdot n^{\frac{1}{2}-\frac{1}{p}} \) linear system solves for p ≥ 2. In this paper, we provide new algorithms for ℓ p -regression (and a more general formulation of the problem) that obtain a high-accuracy solution in O ( pn ( p − 2)/(3 p − 2) ) linear system solves. We further propose a new inverse maintenance procedure that speeds-up our algorithm to \(\widetilde{O}(n^{\omega }) \) total runtime, where O ( n ω ) denotes the running time for multiplying n × n matrices. Additionally, we give the first Iteratively Reweighted Least Squares (IRLS) algorithm that is guaranteed to converge to an optimum in a few iterations. Our IRLS algorithm has shown exceptional practical performance, beating the currently available implementations in MATLAB/CVX by 10-50x.

Science of Moscow University for the Humanities: 80 years of experience as a leading private university

At each stage of the development of the Moscow University for the Humanities, its scientifi c research was determined by the challenges of the time and had a high degree of social and scientifi c demand, which was refl ected in the priority areas approved by the order of the rector. The university provides training in a variety of specialties and areas: economics, management, applied computer science, psychology, pedagogy, law, sociology, international relations, regional studies, journalism, advertising, tourism, hotel management, design, cultural studies, acting and vocalism; therefore, the scientifi c interests of teachers and scientists cover a signifi cant range of those tasks that are a priority for the development of the country. The article discusses priority scientific directions at the Moscow University for the Humanities and the principles of scientific work of the university.

A comprehensive review of community detection in graphs

The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based methods, spectral clustering, probabilistic modeling, and deep learning. Along with the methods, a new community detection method designed by us is also presented. Additionally, the performance of these methods on the datasets with and without ground truth is compared. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs.

Formation of Competences in the Field of Information Security for Future Bachelors of Applied Informatics

The purpose of the study is to develop a model for developing competencies in the field of information security for bachelors in the field of Applied Information Science. The relevance of this problem is determined by modern requirements for enterprise employees and, accordingly, the process of their training. Demand for qualified employees who have skills not only in their field of activity, but also in areas relevant to the information society, such as information culture and the ability to provide an information enterprise.Materials and research methods. To achieve this goal, a competency-based approach was used to activate the process of developing the readiness and ability of subjects to make independent decisions, express independent judgments and at the same time participate in a common cause, create relationships with other participants in joint activities. The following methods were used in the work: analysis of pedagogical, scientific, technical and methodological literature on the research problem, systematization and generalization and visual presentation of the results.Results. As part of the study, a tabular model for the formation of competencies in the field of information security was developed based on the competency-based approach. The article reflects a model for developing competence in the field of information security using the example of additional professional competencies - 2 (the ability to take part in project management, organizing IT infrastructure and information security management) in the process of studying the discipline “Information Security”. The component blocks of the developed model are described: diagnostic, motivational, theoretical, practical and control-analytical. In accordance with the stages of this process of developing competencies and the tasks to be solved at each stage, various innovative forms and methods are presented. The results of the paper show that the process of developing competencies in the field of information security among bachelors in the “Applied Information Science” area is complex and multifaceted. Even though bachelors of applied computer science will not perform the same job functions as information security specialists, their training should be sufficient to enable them to design and build safe and reliable information systems. The pedagogical search is not completed, but it has acquired the necessary boundaries, unique centers of crystallization around which the academic discipline is designed.Conclusion. We can conclude that training specialists in the field of information security is an urgent task of modern society, the solution of which is dependent on scientific justification and identification of a set of necessary competencies that a modern specialist should have. The components of competencies in the field of information security that we have identified, the generalized tabular model of these competencies that we have proposed, as well as the stages, methods and forms of developing these competencies among bachelors of Applied Information Science require additional study. The conducted research allows us to outline ways to solve the multifaceted and complex problem of forming students’ competencies in the field of information security, taking into account modern requirements for the training of specialists not only in the field of computer science and information technology.

Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications

The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

Exploring Clique Transversal Problems for d-degenerate Graphs with Fixed d: From Polynomial-Time Solvability to Parameterized Complexity

This paper explores the computational challenges of clique transversal problems in d-degenerate graphs, which are commonly encountered across theoretical computer science and various network applications. We examine d-degenerate graphs to highlight their utility in representing sparse structures and assess several variations of clique transversal problems, including the b-fold and {b}-clique transversal problems, focusing on their computational complexities for different graph categories. Our analysis identifies that certain instances of these problems are polynomial-time solvable in specific graph classes, such as 1-degenerate or 2-degenerate graphs. However, for d-degenerate graphs where d≥2, these problems generally escalate to NP-completeness. We also explore the parameterized complexity, pinpointing specific conditions that render these problems fixed-parameter tractable.

Transfer Learning for Facial Emotion Recognition on Small Datasets

In the context of human interactions, complexity science (CS) provides a way to understand the dynamics that arise from the interplay of different individuals. Recently, the possibility of applying the theory of CS to computer science, has shifted the focus to the research of machine learning methods for studying human behaviors and relational dynamics. Among the various existing AI techniques, facial emotion recognition (FER) has proven to be the best-performing and easiest to use human- computer interaction (HCI) tool for emotion detection. Despite the numerous existing approaches, the task of FER is not trivial, mainly due to the absence of large enough datasets to train deep learning (DL) models. A widely used solution is transfer learning (TL), which allows a model, pre-trained on enough data, to be used for a specific task where there is much less data available. The aim of our work is to test the effectiveness of TL for FER on an extremely small dataset, to understand which parameters need to be optimised to obtain the best outcomes. The results showed an overall accuracy of 85.54% for our model and revealed the concrete possibility of applying computer science to complex systems typical of the human psyche.

Research on the application of computer science in wind power generation and the future trends

In the context of the growing global demand for clean energy, wind power, as an important form of renewable energy, is gradually occupying a dominant position. At the same time, the continuous development of computer technology has also brought new breakthroughs in wind power generation. Traditional wind power generation suffers from low power generation efficiency and large fluctuations in power generation. However, through the integration of wind power generation with advanced computer technologies, the efficiency and cost of wind power generation have been greatly optimized. This paper seeks to provide an overview of how computer science is utilized in wind power generation, focusing on the integration of computational modeling, simulation, and machine learning. It highlights future trendsand emphasizes the significance of developing digital twins and integrating machine learning with physical models for efficient wind farm management. Combining findings from various existing studies, this paper delves into the profound implications for the future of energy, highlighting the transformative role of computer science in advancing sustainable power solutions and shaping the landscape of renewable energy sources.

Computing edge version of metric dimension of certain chemical networks

In the modern digital sphere, graph theory is a significant field of research that has a great deal of significance. It finds widespread application in computer science, robotic directions, and chemistry. Additionally, graph theory is used in robot network localization, computer network problems and the formation of various chemical structures for networks. Moreover, it finds uses in exploring diffusion mechanisms and scheduling aircraft as well. The present research project examines and concentrates on the edge version of metric dimension of the Concealed Non-Kekuléan Benzenoid Hydrocarbon, Polythiophene, Backbone DNA network and Bakelite networks. All the mentioned networks have constant edge metric dimension except Bakelite network, as demonstrated by the results. If we talk about the applications of these networks, Polythiophene are used to treat prion disorders. It is also capable of detecting metal ions. The concept of Bakelite, which finds applications in the jewelry, electrical, cookware, sports, and clock industries, had an impact on the invention of modern polymers. The functions of DNA include information encoding, replication, mutation, and recombination gene expression.

Online learning system for English speech automatic recognition based on hidden Markov model algorithm and conditional random field algorithm

E-learning provides a flexible way of learning, which can provide corresponding learning content and tasks according to students’ interests, abilities and learning progress, so that students can choose learning content and learning progress more independently. In traditional teaching methods, teachers need to spend a lot of time and energy to evaluate students’ learning. The e-learning system can automatically record students’ learning behavior and learning results, and give feedback and evaluation in time during the learning process. Automatic speech recognition systems have been widely applied in various fields, and the demand for more user-friendly and convenient user operating systems is becoming increasingly urgent. This article proposes an English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm to improve recognition accuracy and stability, and applies it to the computer field. Label inference and optimization for speech recognition were carried out using the conditional random field algorithm. By establishing the correlation between features and labels, label inference was performed on the speech signal, which was compared with existing speech recognition systems to evaluate whether the recognition accuracy and stability of the new system had been improved. The results show that the English automatic speech recognition system based on hidden Markov model algorithm and conditional random field algorithm exhibits high performance in terms of recognition accuracy and stability. Compared with traditional speech recognition systems, this system has made significant improvements in the application of computer science.

Constructing efficient strategies for the process optimization by restart.

Optimization of the mean completion time of random processes by restart is a subject of active theoretical research in statistical physics and has long found practical application in computer science. Meanwhile, one of the key issues remains largely unsolved: how to construct a restart strategy for a process whose detailed statistics are unknown to ensure that the expected completion time will reduce? Addressing this query here we propose several constructive criteria for the effectiveness of various protocols of noninstantaneous restart in the mean completion time problem and in the success probability problem. Being expressed in terms of a small number of easily estimated statistical characteristics of the original process (MAD, median completion time, low-order statistical moments of completion time), these criteria allow informed restart decision based on partial information.